The present disclosure relates to an information processing apparatus, an information processing method, and a program.
In recent years, attention has been focused on a method of mechanically extracting feature amounts from an arbitrary data group for which it is difficult to quantitatively determine features. For example, a method is known for automatically constructing an algorithm that inputs arbitrary music data and mechanically extracts a music genre to which such music data belongs. Music genres such as jazz, classical, and pop are not quantitatively decided by the types of musical instruments or the style of playing. For this reason, it was conventionally thought difficult to mechanically extract the genre of music data when arbitrary music data is provided.
However, in reality, features that decide the genre of a piece of music are latently included in a combination of various information, such as the combination of intervals included in the music data, the way in which the intervals are combined, the combination of types of instruments, and the structure of the melody line and/or the bass line. For this reason, on the assumption that it might be possible to automatically construct an algorithm (or “feature amount extraction device”) for extracting such features through machine learning, research has been conducted into feature amount extraction devices. One result of such research is the automated construction method for a feature amount extraction device based on a genetic algorithm disclosed in Japanese Laid-Open Patent Publication No. 2009-48266. The expression “genetic algorithm” refers to an algorithm that considers selections, crosses, and mutations of elements in a process of machine learning in the same way as in the process of biological evolution.
By using the automated construction algorithm for a feature amount extraction device disclosed in the cited publication, it is possible to automatically construct a feature amount extraction device that extracts, from arbitrary music data, the music genre to which the music data belongs. The automated construction algorithm for a feature amount extraction device disclosed in the cited publication also has extremely wide applicability and it is possible to automatically construct a feature amount extraction device that extracts feature amounts of an arbitrary data group from such data group without being limited to music data. For this reason, there is expectation that the automated construction algorithm for a feature amount extraction device disclosed in the cited publication can be applied to feature amount analysis of manmade data, such as music data and/or video data, and to feature amount analysis of various observed values present in the natural world.